Document Actions

What's special about mining spatial datasets?

Shashi Shekhar, McKnight Distinguished University Professor, and Director of the Army High Performance Computing Research Center, University of Minnesota.

What	MPC Seminar Series
When	February 04, 2008 12:15 PM February 04, 2008 01:15 PM February 04, 2008 from 12:15 pm to 01:15 pm
Where	MPC Seminar Room, 50 Willey Hall
Contact Email	mpc@umn.edu
Contact Phone	612-624-8806
Add event to calendar	vCal iCal

Abstract: The importance of spatial data mining is growing with the increasing incidence and importance of large geo-spatial datasets such as maps, repositories of remote-sensing images, and the decennial census. Classical data mining techniques often perform poorly when applied to spatial data sets because spatial data is embedded in a continuous space (whereas classical datasets are often discrete); spatial patterns are often local (where as classical data mining techniques often focus on global patterns); and spatial data tends to be highly self correlated. For example, people with similar characteristics, occupation and background tend to cluster together in the same neighborhoods. In spatial statistics this tendency is called spatial autocorrelation. Ignoring spatial autocorrelation when analyzing data with spatial characteristics may produce hypotheses or models that are inaccurate or inconsistent with the data set. Thus new methods are needed to analyze spatial data to detect spatial patterns. This talk surveys some of the new methods including those for discovering spatial co-locations, detecting spatial outliers and location prediction.

Sections

Document Actions

What's special about mining spatial datasets?

Local resources